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Join MIT's Youssef Marzouk for a Machine Learning Program Info Session
Learn more about the MIT xPRO Machine Learning online program from Youssef Marzouk, professor in the Department of Aeronautics and Astronautics at MIT, co-director of the CCSE, and director of MIT’s Aerospace Computational Design Laboratory.
Info SEssion Date
September 9, 2026
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TIME
12:00 PM
Duration
45 Minutes
Includes Q&A Session
SIGN UP FOR THE INFO SESSION
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Machine learning is reshaping how engineers approach complex problems— but not every challenge requires it. MIT xPRO's two-course online program takes a practical, hands-on approach to modern computational tools for engineering problem-solving.
In this free info session, MIT Professor Youssef Marzouk will offer an inside look at the Machine Learning, Modeling, and Simulation online certificate program. He will walk through:
- The timeline of computational science and engineering paradigms
- What skills and topics are covered in the popular two-course program
- What the learning experience is like, who should enroll, and the faculty involved
- How the program is helping engineers bridge the gap between traditional methods and new AI-driven techniques
Stick around for a live Q&A to get your questions about the program answered.
Reserve your spot now by filling out the form above. A confirmation email with Zoom access details will follow. A recording will be sent to all registrants following the session.
WHY ATTEND?
Hear directly from an MIT expert about the thinking behind the program and how it addresses real engineering challenges.
Understand what's covered across the two courses, from modeling and simulation to practical uses of machine learning.
Find out who the program is designed for and whether it's the right fit for your professional goals.
Get your questions answered in a live Q&A and engage directly with Professor Marzouk.
MEET YOUR INSTRUCTOR
Youssef Marzouk is a professor in the Department of Aeronautics and Astronautics at MIT and co-director of the CCSE. He is also director of MIT’s Aerospace Computational Design Laboratory.
His research interests lie at the intersection of physical modeling with statistical inference and computation. In particular, he develops methodologies for uncertainty quantification, inverse problems, large-scale Bayesian computation, and optimal experimental design in complex physical systems. His methodological work is motivated by a wide variety of engineering, environmental, and geophysics applications.
